Robot mobility is critical for mission success, especially in soft or deformable terrains, where the complex wheel-soil interaction mechanics often leads to excessive wheel slip and sinkage, causing the eventual mission failure. To improve the rover performance, online mobility prediction using vision, infrared imaging, or model-based stochastic methods have been used in the literature. This letter proposes an on-board mobility prediction approach using an articulated wheeled bevameter that consists of a force-controlled arm and an instrumented bevameter (with force and vision sensors) as its end-effector. The proposed bevameter, which emulates the traditional terramechanics tests such as pressure-sinkage and shear experiments, can measure the contact parameters ahead of the rover's body in real-time, and predict the slip and sinkage of the subsequent supporting wheels over the probed region. Based on the mobility prediction, the rover can select a proper path in order to avoid hazardous regions such as those covered with loose sand. Compared to the literature, our proposed method can avoid the complicated yet inaccurate vehicle-terrain interaction modeling and time-consuming stochastic prediction; it can also mitigate the inaccuracy issues arising in non-contact vision-based methods. We also conduct multiple experiments to validate the proposed approach and the applicability of the articulated bevameter as an on-board equipment to study wheel-terrain interaction mechanics.

The off-road locomotion of wheeled mobile robots (WMRs) over soft terrains can be quite challenging due to the complicated wheel-terrain interaction (WTI). To avoid unforeseen non-geometric hazards such as excessive sinkage or slippage, it is crucial to oversee these terrain-related uncertainties. However, determining the appropriate sensing principle for WTI and hazard prediction remains an open problem. This letter showcases an onboard sensorized transparent wheel concept (STW) aiming to explicitly characterize the WTI over deformable terrains for rovers. The STW configuration can provide directly in-wheel interaction views, thereby offering in-wheel measurement (IM) of WTI parameters and observations of soil flow simultaneously. Unlike traditional vision-based methods, this in-situ wheel vision can characterize the entire contact geometry distributions, eliminating complicated yet inaccurate model-based stochastic estimations. Consequently, it can achieve robust and real-time (30 Hz) performance even under complex motions. We conduct representative terrain experiments on a single-wheel testbed to verify the performance of our proposed STW system, and showcase its applicability as a terramechanics test tool to remodel WTI mechanics, as seen in https://youtu.be/aYKW1Pp4ENw .

Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (SLAM), especially when confronting non-geometric hazards in demanding landscapes. In this paper, we first propose a Terrain-Aware multI-modaL (TAIL) dataset tailored to deformable and sandy terrains. It incorporates various types of robotic proprioception and distinct ground interactions for the unique challenges and benchmark of multi-sensor fusion SLAM. The versatile sensor suite comprises stereo frame cameras, multiple ground-pointing RGB-D cameras, a rotating 3D LiDAR, an IMU, and an RTK device. This ensemble is hardware-synchronized, well-calibrated, and self-contained. Utilizing both wheeled and quadrupedal locomotion, we efficiently collect comprehensive sequences to capture rich unstructured scenarios. It spans the spectrum of scope, terrain interactions, scene changes, ground-level properties, and dynamic robot characteristics. We benchmark several state-of-the-art SLAM methods against ground truth and provide performance validations. Corresponding challenges and limitations are also reported.

Quadruped robots equipped with sensorless feet can only provide limited information regarding the foot interaction with the surroundings, limiting their off-road exploration capability in unstructured environments. To tackle this problem, we present Foot Vision, an innovative vision-based sensorized foot for quadruped robots. It consists of a compliant ankle, a transparent foot sole with visual markers, and an internal camera. This foot is designed to be low-cost, lightweight, and adaptable to both deformable and rigid terrains. The camera can efficiently track marker displacements to estimate the foot pose. We extract the foot deformation features from the foot pose and develop a learning-based method to reconstruct 6D contact forces leveraging these features. Representative loading tests validate the foot's ability to accurately $(R^{2}>0.95)$ and dynamically predict contact forces and torques in real-time $(\text{100}~Hz)$ . Installed on the quadruped robot, the sensorized foot demonstrates its capabilities to perceive 6D contact forces on terrains with varying stiffness and estimate the local inclination of the contact surface. Furthermore, thanks to the transparent foot sole, the in-foot camera can directly identify the terrain types and measure the soil flow distribution. Experimental results confirm that the proposed vision-based sensorized foot can enhance the contact perception capability of legged robots in diverse environments.

Legged robots have been widely used in unstructured field environments, where unforeseen nongeometric hazards, such as sinkage or slippage might compromise their mobility. Such uncertain terrain-related risks may be very challenging to mitigate by employing point or ball-shaped feet, however, determining the appropriate principles for foot design remains an open-research problem. This study presents a systematic pipeline of design and evaluations for the sensorized terrain-adaptive foot (STAF) aiming to remodel the underlying soft, sloped granular media. The advanced foot configuration enables explicit characterization of complex foot-terrain interaction responses, thereby facilitating evidence-based evaluations using proposed terrain physical metrics and terrain traveling metrics. These terrain-aware metrics are interpretable, physics-informed, and versatile for various robot prototypes. Compared to traditional empirical foot design methods, our model-based guideline can reliably identify physical terrain properties and explain contact behavior to optimize the foot sole's primary parameters. We conduct multiple experiments on both the single-foot testbed and a quadruped robot. The findings demonstrate that our developed STAF system significantly enhances mobility and in situ sensing capabilities over loose deformable slopes.

Wheeled mobile robots (WMRs) have been proven to be quite competitive and useful in outdoor missions. However, they may face serious sinkage or slippage on deformable terrains, and even get stuck or damaged, thereby causing mission failure. To mitigate these risks, it is essential to closely monitor the robot's states and its interactions with soft terrains. In this letter, we propose an integrated wheel-terrain interaction detecting algorithm which can measure sinkage, wheel-terrain contact angles, and slip ratio simultaneously online. This vision-based algorithm requires only one fisheye camera as its input. It has strong versatility and can be applied to rovers with different suspensions or morphology variations. We use a single-wheel testbed to verify our proposed algorithm through various experiments. The algorithm achieves a frequency of over 30 Hz, while maintaining a high level of accuracy. The relative error of sinkage and contact angle measurement is around 2%, and the relative error of slip ratio is less than 0.7%. We also propose a semi-empirical model to estimate the wheel sinkage-slip ratio relationship. With its help, we can avoid potential risks to improve the safety and success rate of the robot.